The Pile is a large-scale dataset for language modeling, consisting of 885 GB of text from various sources, including books, articles, and websites.
NLTK provides access to large datasets for language modeling, including the Brown Corpus and the Penn Treebank, which can be used for training and testing NLP models.
This article discusses the challenges and opportunities of using big data for NLP, including the need for large-scale datasets for language modeling and the potential for deep learning models to improve NLP tasks.
Common Crawl is a non-profit organization that provides a large-scale web corpus for NLP research, including a dataset of over 20 TB of text from the web.
This tutorial provides an overview of language modeling with transformers, including how to use large-scale datasets such as the WikiText dataset and the BookCorpus dataset for training transformer models.
The Stanford NLP Group provides access to a range of datasets and tools for NLP research, including the Stanford Question Answering Dataset and the Stanford Sentiment Treebank.
This course provides an overview of large-scale language modeling with deep learning, including how to use datasets such as the Billion Word Benchmark and the One Billion Word Language Modeling Benchmark.
This article provides a comprehensive overview of datasets for NLP, including datasets for language modeling, sentiment analysis, and question answering, and discusses the importance of using large-scale datasets for NLP research.